theHausdorffMetric

require(quantmod)
require(ggplot2)
require(reshape2)

# The standard definitions of boxplots are non-obvious to interpret for non-statisticians. 
# A "the box is fifty percent, the line 95% and there you have 5% outlier points" is
# typically more easily swallowed by practitioners.
# I therefore define two functions which will change the boxplot appearance below.

myBoxPlotSummary <- function(x) {

theHausdorffMetric

require(quantmod)
require(ggplot2)
require(reshape2)
require(plyr)
require(scales)

# Download some Data, e.g. the CBOE VIX 
getSymbols("^VIX",src="yahoo")

# Make a dataframe

theHausdorffMetric

# An simple function to turn an xts time series
# into a ggplot calendar heatmap 
require(tidyverse)

# The core idea is to transform the data such that one can
# plot "Value" as a function of "WeekOfMonth" versus "DayOfWeek"
# and facet this Year versus Month

xts_heatmap <- function(x){
  data.frame(Date=as.Date(index(x)), x[,1]) %>%

theHausdorffMetric

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• I am danielprobst (https://keybase.io/danielprobst) on keybase.
• I have a public key ASAx78zoxGpy8RFqRT5I427AztO4ALheH4N7P-uBKHwYswo

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